Interactive4D: Interactive 4D LiDAR Segmentation
Not on the map. Why?
Abstract (arXiv)
Interactive segmentation has an important role in facilitating the annotation process of future LiDAR datasets. Existing approaches sequentially segment individual objects at each LiDAR scan, repeating the process throughout the entire sequence, which is redundant and ineffective. In this work, we propose interactive 4D segmentation, a new paradigm that allows segmenting multiple objects on multiple LiDAR scans simultaneously, and Interactive4D, the first interactive 4D segmentation model that segments multiple objects on superimposed consecutive LiDAR scans in a single iteration by utilizing the sequential nature of LiDAR data. While performing interactive segmentation, our model leverages the entire space-time volume, leading to more efficient segmentation. Operating on the 4D volume, it directly provides consistent instance IDs over time and also simplifies tracking annotations. Moreover, we show that click simulations are crucial for successful model training on LiDAR point clouds. To this end, we design a click simulation strategy that is better suited for the characteristics of LiDAR data. To demonstrate its accuracy and effectiveness, we evaluate Interactive4D on multiple LiDAR datasets, where Interactive4D achieves a new state-of-the-art by a large margin. We publicly release the code and models at https://vision.rwth-aachen.de/Interactive4D.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 42 of its 56 references that are in the map. First posted on arXiv 10 Oct 2024.
Builds on
42 of 56 references.
References · 42
- 2017Attention Is All You Needcited by 59,932
- 2023Segment Anythingcited by 8,158
- 2019nuScenes: A multimodal dataset for autonomous drivingcited by 4,485
- 2017ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenescited by 3,088
- 2019Scalability in Perception for Autonomous Driving: Waymo Open Datasetcited by 2,436
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
- 2019LVIS: A Dataset for Large Vocabulary Instance Segmentationcited by 1,215
- 20194D Spatio-Temporal ConvNets: Minkowski Convolutional Neural Networkscited by 1,201
- 2021KITTI-360: A Novel Dataset and Benchmarks for Urban Scene Understanding in 2D and 3Dcited by 570
- 2018The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scalecited by 542
- 2020PointContrast: Unsupervised Pre-training for 3D Point Cloud Understandingcited by 502
30 more
- 2020PREDATOR: Registration of 3D Point Clouds with Low Overlapcited by 287
- 2022Language-Grounded Indoor 3D Semantic Segmentation in the Wildcited by 243
- 20183D-SIS: 3D Semantic Instance Segmentation of RGB-D Scanscited by 216
- 2021Panoptic nuScenes: A Large-Scale Benchmark for LiDAR Panoptic Segmentation and Trackingcited by 165
- 2021Reviving Iterative Training with Mask Guidance for Interactive Segmentationcited by 162
- 2016Deep Interactive Object Selectioncited by 160
- 2021Panoptic-PolarNet: Proposal-free LiDAR Point Cloud Panoptic Segmentationcited by 107
- 2022FocalClick: Towards Practical Interactive Image Segmentationcited by 101
- 2020LiDAR-based Panoptic Segmentation via Dynamic Shifting Networkcited by 84
- 2021EfficientLPS: Efficient LiDAR Panoptic Segmentationcited by 76
- 20214D Panoptic LiDAR Segmentationcited by 64
- 2022Panoptic-PHNet: Towards Real-Time and High-Precision LiDAR Panoptic Segmentation via Clustering Pseudo Heatmapcited by 59
- 2020MOPT: Multi-Object Panoptic Trackingcited by 53
- 2018Iteratively Trained Interactive Segmentationcited by 52
- 2022Learning Multi-View Aggregation In the Wild for Large-Scale 3D Semantic Segmentationcited by 47
- 2020A Benchmark for LiDAR-based Panoptic Segmentation based on KITTIcited by 40
- 2021GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Networkcited by 37
- 2019Continuous Adaptation for Interactive Object Segmentation by Learning from Correctionscited by 36
- 20224D-StOP: Panoptic Segmentation of 4D LiDAR using Spatio-temporal Object Proposal Generation and Aggregationcited by 35
- 2023AGILE3D: Attention Guided Interactive Multi-object 3D Segmentationcited by 33
- 2023Mask4Former: Mask Transformer for 4D Panoptic Segmentationcited by 24
- 2022Interactive Object Segmentation in 3D Point Cloudscited by 23
- 20234D Panoptic Segmentation as Invariant and Equivariant Field Predictioncited by 19
- 2023PUPS: Point Cloud Unified Panoptic Segmentationcited by 16
- 2023DynaMITe: Dynamic Query Bootstrapping for Multi-object Interactive Segmentation Transformercited by 14
- 20234D-Former: Multimodal 4D Panoptic Segmentationcited by 12
- 2022LiDAR-based 4D Panoptic Segmentation via Dynamic Shifting Networkcited by 11
- 2024JRDB-PanoTrack: An Open-world Panoptic Segmentation and Tracking Robotic Dataset in Crowded Human Environmentscited by 10
- 2023Lidar Panoptic Segmentation and Tracking without Bells and Whistlescited by 7
- 2024Scale Disparity of Instances in Interactive Point Cloud Segmentationcited by 3
Built on by
Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistencycited by 4
- 2025SAM4D: Segment Anything in Camera and LiDAR Streamscited by 3
- 2025SNAP: Towards Segmenting Anything in Any Point Cloudcited by 0
- 2026Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Modelscited by 0
- 2026Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models26 Aug 2026
- 2025SNAP: Towards Segmenting Anything in Any Point Cloud13 Oct 2025
- 2025SAM4D: Segment Anything in Camera and LiDAR Streams26 Jun 2025
- 2025SALT: A Flexible Semi-Automatic Labeling Tool for General LiDAR Point Clouds with Cross-Scene Adaptability and 4D Consistency31 Mar 2025